AI Engineer

Who we are:

devx Labs is an AI-native consulting firm that fundamentally reimagines business operations around AI capabilities. We don’t just implement AI tools - we transform how enterprises operate through first-principles thinking and outcome-driven execution.

About the role:

• As an AI Engineer, you'll build AI-native solutions that power transformative outcomes across Customer Interactions, AI-Led Business Operations, and Enterprise Architecture.
• Every system you build, every agent you architect, every integration you design directly translates into measurable client value.
• This is not a "make the demo work" role — you're the builder who turns AI potential into production systems that scale, perform, and deliver ROI.

Build AI-Native Solutions (65%)

• Engineer for impact: Design, build, and deploy production-grade agentic AI systems — multi-agent workflows, RAG architectures, LLM-powered automation, and intelligent data pipelines — on our partner platforms (AWS, Gemini Enterprise / Google Cloud).
• Architect with scale in mind: Create solutions that thrive in enterprise environments with thousands of users and millions of transactions.
• Ship velocity: Leverage AI-assisted development to deliver features 2–3x faster than traditional methods while maintaining code quality and reliability.
• Own technical decisions: Choose the right AI stack, model, framework, and architecture — balancing performance, cost, and maintainability.
• Solve across practices: Build customer service agents, inventory intelligence systems, or enterprise integration platforms depending on client needs.

Drive Client Success (25%)

• Translate business to technical: Participate in discovery workshops, understand client pain points, and architect AI solutions that deliver measurable outcomes.
• Demo with confidence: Present technical solutions to client stakeholders, explain architectural decisions, and build trust through technical credibility.
• Iterate with feedback: Work embedded with client teams, gather real-world usage insights, and continuously optimize solutions for adoption and impact.
• Bridge consulting and engineering: Collaborate with Solutions Consultants and Practice Leaders to turn strategy into executable technical roadmaps.

Mentor & Multiply (10%)

• Elevate the team: Guide junior engineers on AI-native development practices, code reviews, architectural thinking, and problem-solving approaches.
• Share knowledge: Document patterns, build reusable accelerators, and create technical playbooks that make the entire team more effective.
• Champion AI-first workflows: Demonstrate how to leverage Claude Code, Cursor, and modern AI tools to achieve 60–70% AI-assisted development velocity.

The Builder Mindset

• First-principles engineer: You don't just implement — you question assumptions, explore alternatives, and design elegant solutions from the ground up.
• Outcome obsessed: You measure success in client impact, not lines of code. Every feature ships with measurable value.
• AI-native practitioner: You already use AI tools daily (Claude Code, Cursor, GitHub Copilot) and think "what can AI do better?" before writing code.

Technical Superpowers

• Agentic AI engineering is a must: hands-on production experience building and deploying agentic systems — multi-agent workflows, tool use / function calling, agent orchestration, evaluation, and the operational side (observability, guardrails, latency and cost management). Prototype-only experience doesn't clear the bar.
• AI/ML engineering depth: Production experience with LLMs, embeddings, vector databases, RAG architectures, and agent frameworks.
• Partner-platform experience: devx labs is a Gemini Enterprise Partner and an AWS Partner — experience shipping on Gemini Enterprise / Vertex AI and AWS Bedrock (or strong depth on one, with the aptitude to pick up the other quickly) is expected.
• Full-stack foundation: Strong Python development plus working knowledge of TypeScript/Node.js and React/Next.js for building complete solutions.
• Cloud-native capability: Experience deploying AI systems on AWS (Lambda, ECS, SageMaker, Bedrock) or GCP (Cloud Run, Vertex AI, Cloud Functions).
• API & integration expertise: Design RESTful APIs, handle webhooks, integrate with enterprise systems, and build reliable data pipelines.

Client-Facing Excellence

• Technical storytelling: Ability to explain complex AI concepts to non-technical stakeholders and align solutions with business objectives.
• Workshop contributor: Comfortable participating in client discovery sessions, solution design workshops, and technical deep-dives.
• Collaborative problem-solver: You work effectively with cross-functional teams — consultants, designers, client engineers — to deliver integrated solutions.

Experience & Background

• 2–6 years of software engineering with at least 1 year focused on AI/ML applications.
• Production AI experience: You've built and deployed LLM-powered and agentic features that real users interact with — including the deployment and operations side, not just the build.
• AI-assisted development: Demonstrable proficiency using AI coding tools (can show GitHub repos, projects, or examples).
• Full-stack capability: Experience building end-to-end features, not just backend or frontend in isolation.
• Client or product experience: Whether consulting, product companies, or startups — you've worked directly with stakeholders to deliver solutions.

• AI tool credits and freedom to experiment with modern approaches.
• Performance-based appraisal at 6 months + annual appraisal cycle.
• Competitive pay with a structured CTC breakup.
• Work with clients across industries and domains.
• Hybrid model with work-from-home support and flexible working hours.
• No-cap leave policy — take time off responsibly, no fixed limits.
• Health insurance with spouse and kids coverage included.
• Yearly outings, and an open environment for innovation.